Automatic morphology phenotyping of tetra- and hexaploid wheat spike using computer vision methods

Pronozin, A Yu; Paulish, A A; Zavarzin, Evgeny A.; Prikhodko, Aleksei; Prokhoshin, N. M.; Kruchinina, Y. V.; Гончаров, Н. П.; Komyshev, Evgenii G.; Genaev, Mikhail A.

doi:10.18699/vj21.009

Cited by 4 publications

(3 citation statements)

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“…MobileNetV2 utilizes the inverted residuals structure, which helps in maintaining a balance between computational efficiency and representational power, it uses linear bottlenecks and shortcut connections to improve information flow. Moreover, BiSeNetV2 (Yu et al, 2021) presented a branching network where the detail branch focused on underlying details using a larger spatial dimension, while the semantic branch captured advanced semantics with large convolutional kernels, these branches were then fused through an aggregation layer, enhancing the model's capabilities. Inspired by these innovations, Yang et al (Yang et al, 2021a) devised a branch network by modifying the VGG16 model.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Plant Breeding

Orlov

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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Section: Deep Learning-based Methodsmentioning

confidence: 99%

Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Plant Breeding

Orlov

2024

Frontiers Research Topics

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“…The best performance was achieved for the random forest model (F1 = 0.85). The spike geometric parameters were later used to compare the spike shape for hexaploid and tetraploid wheat accessions (Pronozin et al, 2021).…”

Section: Related Workmentioning

confidence: 99%

Image-based classification of wheat spikes by glume pubescence using convolutional neural networks

Artemenko,

Genaev,

Epifanov

et al. 2024

Front. Plant Sci.

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IntroductionPubescence is an important phenotypic trait observed in both vegetative and generative plant organs. Pubescent plants demonstrate increased resistance to various environmental stresses such as drought, low temperatures, and pests. It serves as a significant morphological marker and aids in selecting stress-resistant cultivars, particularly in wheat. In wheat, pubescence is visible on leaves, leaf sheath, glumes and nodes. Regarding glumes, the presence of pubescence plays a pivotal role in its classification. It supplements other spike characteristics, aiding in distinguishing between different varieties within the wheat species. The determination of pubescence typically involves visual analysis by an expert. However, methods without the use of binocular loupe tend to be subjective, while employing additional equipment is labor-intensive. This paper proposes an integrated approach to determine glume pubescence presence in spike images captured under laboratory conditions using a digital camera and convolutional neural networks.MethodsInitially, image segmentation is conducted to extract the contour of the spike body, followed by cropping of the spike images to an equal size. These images are then classified based on glume pubescence (pubescent/glabrous) using various convolutional neural network architectures (Resnet-18, EfficientNet-B0, and EfficientNet-B1). The networks were trained and tested on a dataset comprising 9,719 spike images.ResultsFor segmentation, the U-Net model with EfficientNet-B1 encoder was chosen, achieving the segmentation accuracy IoU = 0.947 for the spike body and 0.777 for awns. The classification model for glume pubescence with the highest performance utilized the EfficientNet-B1 architecture. On the test sample, the model exhibited prediction accuracy parameters of F1 = 0.85 and AUC = 0.96, while on the holdout sample it showed F1 = 0.84 and AUC = 0.89. Additionally, the study investigated the relationship between image scale, artificial distortions, and model prediction performance, revealing that higher magnification and smaller distortions yielded a more accurate prediction of glume pubescence.

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“…At the same time, it is necessary to substantially reduce the cost and time of obtaining relevant data with the maximum coverage, resolution and dynamics. For the diagnosis of the state of crops, it is promising to use drones (Alt et al, 2019); for the diagnosis of plants, automatic morphology phenotyping (Pronozin et al, 2021b); for a risk assessment of edited plant organisms, dynamic programming (Korotkov et al, 2021).…”

Section: Bioinformaticsmentioning

confidence: 99%

Scientific support to plant breeding and seed production in Siberia in the XXI century

Гончаров

2021

Vestn. VOGiS

Self Cite

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Agriculture in the Russian Federation is fundamental to the country’s economic performance, living standards, the wellbeing of people and state safety. Considerations relating to food security, prospects of and challenges before plant breeding in the Siberian Federal District (SFD), the largest agricultural area of the Russian Federation, are provided in the article. The agricultural area used in the SFD is about 50 million hectares and accounts for 13 % of the country’s gross grain production. The need for the introduction of modern molecular biological methods, bioengineering and IT technology is demonstrated and discussed. As Russia as a whole, Siberia is largely engaged in unpromising extensive farming practices, which rely on natural soil fertility, and this factor should be taken into account. Another issue is noncompliance with intensive farming technologies used for cultivating new-generation commercial cultivars. Although capital investments in plant breeding are the most cost effective investments in crop production, breeders’ efforts remain underfunded. The article explains the need for fundamental reform in this economic sector: the recognition of plant breeding as being a fundamental science; a fair increase in its funding; the development of a breeding strategy, nationally and regionally; the further expansion of the network of the Breeding Centers; the re-establishment and improvement of the universities’ departments specialized in plant breeding and seed production; having more state-funded places in the universities for training plant breeders to be able to maintain and cement the country’s advanced position in plant breeding and to develop new globally competitive next-generation cultivars of main crops. Should these issues be ignored, all the problems that have accumulated to date will lead to risks of long-term instability in this economic sector. The need for the careful preservation of continuity in plant breeders and plants being bred is stated. The regulatory functions of the state and agricultural science in plant breeding, plant industry and seed production are considered.

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Automatic morphology phenotyping of tetra- and hexaploid wheat spike using computer vision methods

Cited by 4 publications

References 23 publications

Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Plant Breeding

Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Plant Breeding

Image-based classification of wheat spikes by glume pubescence using convolutional neural networks

Scientific support to plant breeding and seed production in Siberia in the XXI century

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